1. Field of the Invention
The present invention relates to the technical field of pipeline network internal detection method, and particularly to an intelligent adaptive system and method for monitoring leakage of oil pipeline networks based on big data.
2. The Prior Arts
Pipeline transportation is an economical and convenient mode of transportation. Compared with other modes of transportation, pipeline transportation has the advantages of high efficiency, security, economy, and convenient control and management, therefore playing an important role in fluid conveying. In accordance with the “12th Five-Year Plan”, the total length of gas and oil pipelines in China will reach about 150 thousand km by the end of 2015, among which: 25 thousand km of oil pipelines and 44 thousand km of gas pipelines will be newly built. However, accidents of pipeline leakage often occur due to the ageing of pipeline devices, the change of geographical conditions and the damage caused by human factors. When the accident of pipeline leakage occurs, it may not only cause explosion and fire, but also cause personal casualty. Therefore, it has great social significance and economic benefits to monitor fluid conveying pipelines in time and take corresponding emergency measures to prevent further expansion of leakage accident.
There are a plurality of methods for detecting leakage of fluid conveying pipelines, mainly including external environment detection, pipe wall detection and pipeline internal flow condition detection, wherein pipeline internal flow condition detection is the main method for detecting and locating leakage at present, and it further includes state model method, sound-wave-based method, pressure point analyzing method, negative pressure wave method, etc. Systems for detecting pipeline leakage based on pressure signals have been widely used, but these kinds of systems still have some common problems at present: first, missed alarm rate of small amount of leakage and slow leakage is high; second, the resistance of the systems against working condition disturbance is not strong, and false alarm rate is high.
At present, research on method for detecting and locating leakage of single pipelines has been relatively mature, but in engineering practice, there is a plurality of oil pipelines with one or more branches, namely pipeline networks. Information obtained from oil pipeline networks has the characteristics of large information amount and large data amount, and the signal collection of pressure, flow rate, etc. belongs to millisecond data, which fully reflects the characteristics of big data. Moreover, temperature and density can also be collected. In addition, the structures of oil pipeline networks are complex, which further increases the difficulties in detecting leakage of oil pipeline networks. Whereas the research on conveying by pipeline networks at present basically rests on conveying by single pipelines, it is not able to well grasp the analysis as a whole to detect the pipeline networks. Furthermore, during conveying by pipeline networks, the influence of working condition disturbance and system noise on pressure waves during propagation is greater, and pressure signal attenuation will be more severe, therefore, leakage detection sensitivity and location accuracy will be greatly decreased.